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            Railroad Trespass Detection Using Deep Learning-Based Computer Vision
      

      
  Document Series
              Research Results


      
  Author
          Federal Railroad Administration


      
Report Number
              RR 22-04

  Office
              RDI-23


      Subject
                    Grade Crossing Modeling and Simulation,
                    Grade Crossing Technology,
                    Highway-Rail Grade Crossings


      
  Keywords
              Automated railroad trespass detection, artificial intelligence, AI, machine learning, railroad trespassing, grade crossing violations, railroad safety


      

      
      
              
                  
      
              
  Document
              AI GX Trespass-A.pdf (539.79 KB)

      
              The U.S. Department of Transportation John A. Volpe National Transportation Systems Center (Volpe), under the direction of the Federal Railroad Administration (FRA) Office of Research, Development, and Technology, developed an AI software application for automating the detection of grade crossing violations and trespass activities from static camera video feeds. The Grade Crossing Trespass Detection GTCD software application outputs predicted grade crossing violations and right-of-way trespassing as tabular data in MS Excel format along with annotated video files of trespass events. Accurately detecting when a trespass event occurs using standard video input reduces the time needed to collect safety data. Currently, railroads and many state DOTs have a wealth of video data on their systems, but that data is generally only analyzed if there is a documented incident. Automated identification and processing of trespass events from the existing video data may yield significant safety data currently not being analyzed.
      
            
      
      
              Last updated: Tuesday, February 8, 2022 U.S. DEPARTMENT OF TRANSPORTATION

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